2003 Digital Symposium Collection

CMVF: A Novel Dimension Reduction Scheme for Efficient Indexing in A Large Image Database

Jialie Shen, Anne H. H. Ngu, John Shepherd, Du Q. Huynh, and Quan Z. Sheng
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Abstract

. This method incorporates both PCA and non-linear neural network techniques to reduce the dimensions of feature vectors, so that an optimised access method can be applied. In this demonstration, we show that with the CMVF approach a small but well-discriminating feature vector can be obtained for effective indexing. It allows us to incorporate classification information based on human visual perception into the indexing. In addition, the effectiveness of indexing can be improved signi cantly by the integration of additional image features.

BIBTEX

@inproceedings       {DBLP:conf/sigmod/ShenNSHS03,
  author    = {Jialie Shen and
                Anne H. H. Ngu and
                John Shepherd and
                Du Q. Huynh and
                Quan Z. Sheng},
   booktitle = {SIGMOD Conference},
   title     = {CMVF: A Novel Dimension Reduction Scheme for Efficient Indexing in A Large Image Database.},
   pages     = {657},
   year      = {2003},
   url       = {db/conf/sigmod/sigmod2003.html#ShenNSHS03},
   ee        = {http://www.acm.org/sigmod/sigmod03/eproceedings/papers/dem01.pdf},
   crossref  = {conf/sigmod/2003},
   bibsource = {DBLP, http://dblp.uni-trier.de}
}